Communication Technology and Application of Seismic Precursor Network Instrument

In this paper, the communication technology of seismic precursor network instrument is introduced, including instruction format and returned information format of instrument login, status information acquisition, and current measured data acquisition. The remote monitoring alarm software is based on this technology, and also introduced that the structure of monitoring information table, abnormal alarm index, and monitoring strategy. The application of the software raises instrument running rate and observation data quality.

Several Key Issues in Networking Management of Analytic Instruments

To improve the application of networking to analytic instruments, a kind of networking method based on conversion module between serial port RS-232 and protocol TCP/IP of MOXA Company is studied and compared with traditional methods. Several characteristic classifications of equipment are summarized, and the methods of relevant data treatment are introduced. The key technical issues and successful cases are also presented which provides the networking of equipment in laboratories with a flexible and highly efficient solution.

Network Reliability Analysis of Digital Instrument and Control System with GO-FLOW Methodology

In this paper,the network reliability of an actual digital instrument and control system (DICS) network is analyzed by using GO-FLOW methodology (GFM).The evaluations of common-cause failure (CCF) and uncertainty are incorporated.Three significant CCF groups (real time servers,gateways,reactor protection system) and three typical time intervals (10 min,1 h,and 24 h) are selected in the analysis.It is concluded that the network contribution of CCF accounts for over 68% of the system failure probability.The result indicates that GFM is suitable for the network reliability analysis.

Design and Analysis of a Network Traffic Analysis Tool: NetFlow Analyzer

A network analyzer can often comprehend many protocols, which enables it to display talks taking place between hosts over a network. A network analyzer analyzes the device or network response and measures for the operator to keep an eye on the network’s or object’s performance in an RF circuit. The purpose of the following research includes analyzing the capabilities of NetFlow analyzer to measure various parts, including filters, mixers, frequency sensitive networks, transistors, and other RF-based instruments. NetFlow Analyzer is a network traffic analyzer that measures the network parameters of electrical networks. Although there are other types of network parameter sets including Y, Z, & H-parameters, these instruments are typically employed to measure S-parameters since transmission & reflection of electrical networks are simple to calculate at high frequencies. These analyzers are widely employed to distinguish between two-port networks, including filters and amplifiers. By allowing the user to view the actual data that is sent over a network, packet by packet, a network analyzer informs you of what is happening there. Also, this research will contain the design model of NetFlow Analyzer that Measurements involving transmission and reflection use. Gain, insertion loss, and transmission coefficient are measured in transmission measurements, whereas return loss, reflection coefficient, impedance, and other variables are measured in reflection measurements. These analyzers’ operational frequencies vary from 1 Hz to 1.5 THz. These analyzers can also be used to examine stability in measurements of open loops, audio components, and ultrasonics.

Realization of Network Bypass Protecting Instrument Based on ARM-Linux

With the control mode of value-added network services equipment changing from bypass intervention to serial control, in order to minimize the security problems which are caused by the failure of the network equipment, this paper proposes a stable, fast and intelligent network bypass protection system combined with the ARM embedded system—Linux platform. In practical application, the system can automatically skip the broken-down device node to protect the network to be unobstructed. With the features of fast response, stable operation, low power consumption, and the flexible of combination, the scheme is proved to have large practical value.

基于LSTM网络的单台仪器地震烈度预测模型

烈度是地震预警系统的关键产出.如何实现快速预测目标场址的地震烈度是地震预警方法技术研究中的核心问题.本文提出了一种基于长短时记忆神经网络(Long Short-Term Memory,LSTM)的单台仪器地震烈度的预测模型(LSTM-Ⅰ).该模型以一个台站观测到地震动参数的时间序列特征为输入,实现动态预测该台站可能遭受的最大烈度.选取了日本K-NET台网记录的102次地震的5103条强震加速度记录训练了神经网络,利用89次地震的3781条数据检验了模型的泛化能力.利用准确率、漏报率以及误报率三个评价指标评价了LSTM-Ⅰ模型的性能.结果表明,当采用P波触发后3 s的序列进行预测时,模型出现漏报的概率为46.78%,出现误报的概率为1.25%;当采用P波触发后10 s的序列进行预测时,模型出现漏报的概率大幅降低到17.6%,出现误报的概率降低到1.14%.结果表明LSTM-Ⅰ模型很好把握住了时间序列中蕴含的特征.进一步基于LSTM-Ⅰ模型评估了Ⅵ度下台站所能提供的预警时间.本文模型能够提供的预警时间与P-S波到时差接近,说明LSTM-Ⅰ模型具有较高的时效性.

基于物理信息神经网络的金属表面吸收率测量方法

漫反射金属吸收率的准确测量十分重要且比较困难.量热法可靠性较高,但是参数反演较为困难.为此,建立了一种物理信息神经网络方法.该方法通过神经网络拟合温度上升段曲线,进而获得吸收率.为了验证该方法,开展了数值仿真和实验研究.数值仿真结果表明,该方法适用于吸收率测量,抗干扰能力强,反演精度高,在0.05—0.2的吸收率范围内,最大误差为0.00092.实验以喷砂镀金铝板为被测对象,受表面粗糙度、镀金工艺等影响,这些样品的吸收率处于2%—10%之间,测量重复精度优于1%.基于物理信息神经网络的吸收率测量方法有望成为一种有力的金属表面吸收率测量方法.

压力变送器研究现状及发展趋势

压力变送器作为工业领域的重要测量仪器,已广泛应用于火电、核电、化工、钢铁、航天等领域。随着检测技术和自动化技术的发展,对压力变送器的稳定性、精确度以及智能化水平提出了更为严苛的要求。介绍了压力变送器的主体结构和常见分类。从通信方式、特殊环境测量要求以及智能化等多个方面分析了压力变送器的研究和应用现状。压力变送器的通信方式已经从传统的模拟通信发展到方便、快捷的无线通信。针对高温极端环境压力测量,更多的新型材料(如碳化硅、石墨烯等)被用作传感器敏感元件。为了适应智能化需求,一些研究者设计、研发了具有自诊断功能的压力变送器,以保障系统的安全性。为了克服压力传感器的温度漂移问题,提出了各种神经网络和深度学习的温度补偿算法。无线化、智能化、高精度等将成为压力变送器未来的主要发展方向。

院际仪器共享平台构建共同网络管理系统的探索

高校不同院系仪器共享平台构建共同的大型仪器共享网络管理系统,意在提升大型仪器管理水平和共享力度,促进跨院系、跨学科的教学、科研工作。共享网络管理系统实现各仪器平台内大型仪器设备在该系统内管理和共享,同时系统允许各仪器平台依据自身情况拥有部分项目的个性化管理权限。通过立足共享网络管理系统,从技术队伍建设、质量体系建设、大数据深度挖掘、移动终端建设、服务功能拓展等方面,思考探索如何提高大型仪器的共享力度和共享效益。

智能网联汽车仪表的人机交互界面设计

如何提高智能网联汽车仪表界面的使用可靠性、改善其人机交互质量,是目前汽车行业面临的主要问题之一。首先,从车辆自动驾驶以及车联网智能终端使用的角度出发,挖掘智能网联汽车对仪表界面设计的要求,然后,讨论了汽车仪表界面的设计原则及仪表界面交互流程设计,最后,设计了汽车仪表人机界面,以提升用户的驾驶体验,保证了汽车的行驶质量。该设计对未来智能网联汽车仪表的人机交互界面的设计研究具有一定的借鉴意义。

人工神经网络与计算技术在电子仪器运行维护中的应用与优化

电子仪器设备是电气工程中的重要组成部分,常见的电子仪器设备包括断路器、变压器、输电线路等,对电气工程质量起到决定性影响。随着互联网与信息技术的快速发展,人工智能技术得到了广泛发展和利用,其能够提高运维决策的精确性和处理效率,从而确保运维质量和稳定性。基于此,本文针对人工神经网络和计算技术在电子仪器运行维护中的应用与优化方式进行分析,从而为电气工程的顺利开展提供更好的保障。

镇海炼化扩建120万吨/年乙烯项目SIS系统网络安全等级保护项目

镇海炼化扩建120万吨/年乙烯项目SS系统网络安全等级保护项目是中石化行业内第一套实施并投用的网络安全等级保护2.0项目。北京康吉森自动化技术股份有限公司作为此安全仪表系统的实施单位,成功完成该项目并满足设计要求的各项指标。

安全仪表系统及其在城镇燃气门站中的运用

在供气管网系统中,燃气门站起到了十分关键的作用,直接关乎到城镇供气的安全与稳定。因此,在城镇燃气门站设计中,应结合运营风险、定量风险、可操作性等方面,科学选择控制方案,合理应用安全仪表系统,确保燃气输送管网运行安全。同时,在确保管网安全的基础上,尽可能降低站场系统误操作,确保整个供气系统运行的安全性。基于此,本文重点探究安全仪表系统在城镇燃气门站中的应用。

基于粒子群算法的汽车仪表框架注塑工艺参数优化

以某汽车仪表框架为例,对塑件注塑后的翘曲量进行研究。选取模具温度、熔体温度、冷却时间、保压压力、保压时间和注射时间作为自变量,以翘曲量作为优化对象。通过正交试验得到各个工艺参数范围内翘曲量较小的工艺参数组合,利用BP神经网络建立塑件翘曲量和各工艺参数之间的非线性模型。将BP神经网络模型作为粒子群算法的适应度函数,通过粒子群算法的优化能力对工艺参数进行优化,并得到最小的翘曲量0.9363 mm和最优的工艺参数组合,即模具温度55.813℃,熔体温度259.568℃,冷却时间29.650 s,保压压力85.02 MPa,保压时间29.187 s,注射时间1.23 s。利用Moldflow模流分析软件对最优的工艺参数组合进行翘曲分析,得到翘曲量为0.9489 mm。与初始默认值和正交试验优化的工艺参数结果对比,翘曲量分别降低了64.48%和19.17%,达到优化塑件质量的目的。

利用深度学习与物理特征预测仪器地震烈度

快速准确地预测记录台站的仪器地震烈度,并在地震预警过程中向潜在用户提供及时和准确的警报至关重要.本文提出了一种结合深度学习和物理参数特征的算法,用于更可靠地预测仪器地震烈度.收集并处理了2001—2021年日本K-NET和KiK-net强震仪台网记录的3386次地震事件作为研究样本.对这些地震事件进行截取、基线校正、质量筛选等预处理,共得到25714条三通道地震波形.使用18000条地震记录(90%训练,10%验证)构建了一个窗长为3秒的仪器地震烈度预测模型(CNN-PP),并对7714条地震记录进行了测试.结果表明,CNN-PP模型在预测仪器地震烈度方面优于传统的单一特征参数方法.此外,离线震例测试结果显示,CNN-PP模型的报警成功率达到95.03%,没有出现误报情况,为解决地震预警仪器地震烈度的测定提供了一种潜在方法.

通风机械仪表盘在复杂背景环境中视觉故障检测与定位研究

通风机械仪表盘往往处于复杂的背景环境中,阴影或部分遮挡会在图像中引入不一致的颜色、亮度和纹理变化,使得故障区域与周围环境的对比度下降,导致人工方法难以正确定位故障区域;针对这些问题,设计一种通风机械仪表盘视觉故障检测与定位方法;使用Kinect相机提取通风机械仪表图像,并进行直方图均衡化来调节图像的亮度和色调,增强故障轮廓与背景的局部对比度;利用改进像素相关性分割算法分割图像特征,将图像中的仪表盘区域从复杂背景中提取出来;利用深度学习领域的深度卷积网络,对分割后的仪表盘图像进行故障轮廓检测;计算定位目标(故障轮廓)的质心坐标,将质心位置作为目标点,映射到构建的投影成像空间坐标系中实现对仪表盘显示故障区域的高精度定位;实验结果表明:应用该方法后,故障区域与周围环境的对比度区分显著增强,具有较高的检测和定位精度。

Automatic Recognition Method for Optical Measuring Instruments Based on Machine Vision

Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope(UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation(BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dy-namically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.

Delay Trigger in the Application of Network Testing System

The researchers who study the local area network( LAN) eXtension for instrumentation( LXI) instrument are pursuing instrument's high-precision synchronization. In the paper,three synchronization modes were discussed which were clock synchronization, trigger synchronization, and response synchronization. Synchronous process between LXI instruments was analyzed and each time factor affecting the synchronization accuracy was discussed. On the basis of the analysis,it can be found that delay trigger plays an important role in the network testing system's synchronization. Delay trigger can produce an additional time interval to correct the difference of each LXI instrument's response time. Then,a method to realize the delay trigger was introduced. Delay time can be adjustable according to the actual demand. Finally,synchronization accuracy of network testing system can reach nanoseconds.

Method of Judging Potential Failure States of Electronic Instruments

Potential failures of electronic instrument are very common in the engineering practice.In this paper,potential failure state model is analyzed based on dynamic characteristics of electronic instrument at work and a comprehensive method of judging multi-state reliability is put forward.Then,a multi-state electronic instrument reliability analysis model is built based on Bayesian Networks(BN).Considering the failure-potential failure-normal work states,the model is built to estimate reliability of the system and the conditional probability of the elements.Finally,the model is proved corrective and effective by examples.

Fuzzy-Logic Based Multi-Sensory Quality Evaluation via Communication Network

In this paper, a multi-sensory quality evaluation using an array of instruments to measure different sensory qualities is established via communication network. The network is used to transmit quality data to evaluation computer. And the network-induced delays between instruments and computer may have negative influence on final evaluation results. The main goal of this paper is to analyze network delays’ influence on evaluation results, and present a fuzzy-logic based solution to eliminate the impact and improve the precision of evaluation. And simulations are conducted to show the effectiveness of the proposed approach.